Images of ten rice genotypes consisting of Chithrakar, Kuliyadichan, TRY 4, ADT 53, ACK 14090, ACK 15004, ADT 45, ASD 19, IR 64, Bhavani were selected and subjected to various image processing machine learning tool for vision based classification. Using Grain analyzer, morphological features of rice seed such as length, breadth, thickness, geometric mean diameter, sphericity, surface area, weight and area were estimated. The mean data were subjected to PCA analysis in STAR software to reduce the dimensionality. The trait such as length, surface area, geometric mean diameter, area and weight contributed significant variation as they possessed positive values in both PC1 and PC 2. The predicted variables of PCA and visual, textural, spectral characteristics of rice seed image obtained from Image Analyser (LEICA) were subjected to various image processing process. The processed images were fed into the machine tools viz., Partial Least Square Regression (PLS) and Support Vector Machine (SVM) for vision based classification. Totally 2000 images were taken and 80 per cent images were used for training the model, 20 per cent images were kept for testing the model. By comparing accuracy, precision, recall and F1 score of both the methods, PLS gives better performance than the SVM classifier. By using these classifiers, genotypes could be identified based on morphological features, visual characteristics and textural characteristics, as the accuracy and prediction are reliable.